Custom Containers
Last updated
Last updated
The Custom Containers feature lets you pull your own image from a container registry e.g., Docker Hub. This section will help you prepare a custom Docker container and show you how to bring that Container into Gradient by creating either a Notebook, Workflow, or Deployment with your custom container.
A computer with , , and NVIDIA Drivers installed (if you don’t have a Linux machine, use a !).
From that machine, you'll need to be logged into your account in the terminal.
docker login -u <username> -p <password>
You can make your own Docker file from scratch (see "Requirements for for Custom Notebooks" below) or use another as a basis, such as this . For now, let's follow along this provided sample.
First, clone the repo, and navigate to the corresponding directory in your terminal.
Open the Docker file into your IDE of choice, and make edits as necessary. The provided Docker file sample above pulls from DockerHub a Paperspace-made TensorFlow image designed to run TensorFlow 2.0 for GPUs on Gradient. You can do so with the FROM instruction.
Example: FROM docker.io/tensorflow/tensorflow:latest-gpu-jupyter
Once your Dockerfile is written, save it, and navigate back to the terminal.
Now that the Docker file is written, we can build the image by running:
docker build -t <Name of image>
For the example file above, you could enter:
docker build test-container
Once you have created the image, you need to tag it so that it can be added to Docker Hub. Tag the image using the image id, your Docker Hub username, and a name for the image in the following format:
docker tag <Image id> <Docker username>/test-container:<Tag>
Now that we have created and tagged out container, we can push it to Docker Hub for use with our Gradient resources. Use the following docker push command to do so:
docker push <Docker username>/test-container:<tag>
The following should be included in your Docker file to ensure the Notebook will set up and run properly.
Python:
RUN apt-get update && apt-get install -y python3 python3-pip
If you don't specify a user, your container user will be 'root'
After you've pushed your custom container to Docker Hub, NGC, etc., or you found a public container that is already there, it's time to pull it into Gradient!
On the Choose Container step, navigate to the custom container tab and fill out the form. Note: A username and password must be provided for private Docker images.
Jupyter and all of Jupyter's dependencies:
RUN pip install jupyterlab
(For more details, see )
Click the advanced options toggle on the notebook create a notebook page. Follow the rest of the steps to create your Notebook by selecting your machine type, naming your notebook, and clicking Create.
Just specify the path of the container e.g., paperspace/gradient-sdk from within a Workflow step using image. Learn more .